<ul><li>Researchers explore implicit inductive bias in factorized VAEs for learning disentangled representations.</li><li>Analysis of total correlation reveals a crucial bias called disentangling granularity in VAEs.</li><li>Findings show that adjusting disentangling granularity affects the range of disentangled features, leading to improved performance.</li><li>The study sheds light on how disentangling granularity influences VAEs' interpretability and biases.</li></ul>

Disentangling Granularity: An Implicit Inductive Bias in Factorized VAEs

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